{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:TWWANWJB6TGEDGE6GRQXP27FDE","short_pith_number":"pith:TWWANWJB","schema_version":"1.0","canonical_sha256":"9dac06d921f4cc41989e346177ebe5190e5ec00e5ddae515b1d684edf715a56e","source":{"kind":"arxiv","id":"2608.13031","version":1},"attestation_state":"computed","paper":{"title":"UniTraffic-Agent: Unified Traffic Video Reasoning for AI City Challenge 2026 Track 3 with Two Out-of-Domain Evaluations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Peng Li, Qianqian Xu, Qingming Huang, Shilong Bao, Yangbangyan Jiang","submitted_at":"2026-08-13T10:00:27Z","abstract_excerpt":"Traffic video understanding has become an important problem in intelligent transportation, as road videos provide direct evidence for accidents, violations, and interactions between vehicles and vulnerable road users. A useful system should explain how a traffic event develops, why it happens, and when the relevant interaction occurs, yet this remains difficult for multimodal large language models (MLLMs) because traffic videos contain sparse events and varied viewpoints. We introduce UniTraffic-Agent, the MR-CAS solution for Track~3 of the 10th AI City Challenge, which includes Traffic Anomal"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2608.13031","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-08-13T10:00:27Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"9b81349a58013c3dff74711a0c3ff9caf946e073a03c56c4f8f02ff7ba32f6e1","abstract_canon_sha256":"4ff879193b0f37c14c93fd18c7ee224a298957e48483c2eec52e771bc4dfc265"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-14T01:00:10.177739Z","signature_b64":"EMo+OtN+XyWe/86EoqMhZoFVV6gAzSG8l1Nd4TLfwg2QPz+OTlJQkV/7qmr0BQYl5o9b56z+QU6hgDKpLxukAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9dac06d921f4cc41989e346177ebe5190e5ec00e5ddae515b1d684edf715a56e","last_reissued_at":"2026-08-14T01:00:10.175751Z","signature_status":"signed_v1","first_computed_at":"2026-08-14T01:00:10.175751Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"UniTraffic-Agent: Unified Traffic Video Reasoning for AI City Challenge 2026 Track 3 with Two Out-of-Domain Evaluations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Peng Li, Qianqian Xu, Qingming Huang, Shilong Bao, Yangbangyan Jiang","submitted_at":"2026-08-13T10:00:27Z","abstract_excerpt":"Traffic video understanding has become an important problem in intelligent transportation, as road videos provide direct evidence for accidents, violations, and interactions between vehicles and vulnerable road users. A useful system should explain how a traffic event develops, why it happens, and when the relevant interaction occurs, yet this remains difficult for multimodal large language models (MLLMs) because traffic videos contain sparse events and varied viewpoints. We introduce UniTraffic-Agent, the MR-CAS solution for Track~3 of the 10th AI City Challenge, which includes Traffic Anomal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.13031","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2608.13031/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2608.13031","created_at":"2026-08-14T01:00:10.176734+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.13031v1","created_at":"2026-08-14T01:00:10.176734+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.13031","created_at":"2026-08-14T01:00:10.176734+00:00"},{"alias_kind":"pith_short_12","alias_value":"TWWANWJB6TGE","created_at":"2026-08-14T01:00:10.176734+00:00"},{"alias_kind":"pith_short_16","alias_value":"TWWANWJB6TGEDGE6","created_at":"2026-08-14T01:00:10.176734+00:00"},{"alias_kind":"pith_short_8","alias_value":"TWWANWJB","created_at":"2026-08-14T01:00:10.176734+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TWWANWJB6TGEDGE6GRQXP27FDE","json":"https://pith.science/pith/TWWANWJB6TGEDGE6GRQXP27FDE.json","graph_json":"https://pith.science/api/pith-number/TWWANWJB6TGEDGE6GRQXP27FDE/graph.json","events_json":"https://pith.science/api/pith-number/TWWANWJB6TGEDGE6GRQXP27FDE/events.json","paper":"https://pith.science/paper/TWWANWJB"},"agent_actions":{"view_html":"https://pith.science/pith/TWWANWJB6TGEDGE6GRQXP27FDE","download_json":"https://pith.science/pith/TWWANWJB6TGEDGE6GRQXP27FDE.json","view_paper":"https://pith.science/paper/TWWANWJB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.13031&json=true","fetch_graph":"https://pith.science/api/pith-number/TWWANWJB6TGEDGE6GRQXP27FDE/graph.json","fetch_events":"https://pith.science/api/pith-number/TWWANWJB6TGEDGE6GRQXP27FDE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TWWANWJB6TGEDGE6GRQXP27FDE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TWWANWJB6TGEDGE6GRQXP27FDE/action/storage_attestation","attest_author":"https://pith.science/pith/TWWANWJB6TGEDGE6GRQXP27FDE/action/author_attestation","sign_citation":"https://pith.science/pith/TWWANWJB6TGEDGE6GRQXP27FDE/action/citation_signature","submit_replication":"https://pith.science/pith/TWWANWJB6TGEDGE6GRQXP27FDE/action/replication_record"}},"created_at":"2026-08-14T01:00:10.176734+00:00","updated_at":"2026-08-14T01:00:10.176734+00:00"}